[DB Seminar] Fall 2017: Joy Arulraj

Speaker:

Joy Arulraj

For the first time in 25 years, a new non-volatile memory (NVM) category is being created that is two orders of magnitude faster than current durable storage devices. The advent of NVM fundamentally changes the dichotomy between memory and durable storage in database systems. These new NVM devices are almost as fast as DRAM, but all writes to it are... Read More

[DB Seminar] Fall 2017: Capstone Project Practice Talks

Speakers:

Shuyao Bi, Patrick Huang, Chen Luo, Prashasthi Prabhakar, Min Huang

Five of the DB group's Masters students will present their Capstone Projects: Building in-memory DBMS cost model in Peloton (Shuyao Bi) Extend Query Optimizer in Peloton (Patrick Huang) Robust Hash Join Execution (Chen Luo) JIT Compilation of User-Defined Functions in High Performance Database Management Systems (Prashasthi Prabhakar) ART index and index scan code gen (Min Huang) Read More

TimescaleDB: Re-engineering PostgreSQL as a Time-series Database (Michael Freedman)

Speaker:

Michael J. Freedman

System:

TimescaleDB

Video:

YouTube

Time-series data is now everywhere—IoT, user event streams, system monitoring, finance, adtech, industrial control, transportation, and logistics—and increasingly used to power core applications. It also creates a number of technical challenges: to ingest high volumes of structured data; to ask complex, performant queries for both recent and historical time intervals; to perform specialized time-centric analysis and data management. And this... Read More

[Fall 2017] Peloton Hack-a-thon

The CMU Database Group is holding an all day programming session for the Peloton database system project. The goal of this event is to provide an introduction to the system and teach students how to get started with the project. The day will begin with an overview of the system and build/test it. We will then teach everyone how to... Read More

QuasarDB: Internals, What Makes a Database Fast? (Edouard Alligand)

Speaker:

Edouard Alligand

System:

QuasarDB

Video:

YouTube

QuasarDB is a scalable timeseries database that was designed to handle the extreme use cases one can find, for example, in market finance. In this talk we will see a couple of design and implementation decisions that were made to deliver the performance QuasarDB today delivers, especially regarding network communications, memory management and real-time aggregation. Read More

Time Series Analytics for Streaming Big Fast Data (Fintan Quill)

Speaker:

Fintan Quill

System:

kdb

Video:

YouTube

Trying to solve the data riddle purely through the lens of architecture is missing a vital point: The unifying factor across all data is a dependency on time. The ability to capture and factor in time is the key to unlocking real cost efficiencies. Whether it’s streaming sensor data, financial market data, chat logs, emails, SMS or the P&L, each... Read More

Alex Benik (Battery Ventures)

Speaker:

Alex Benik

Alex Benik is a Partner at Battery Ventures. The talk promises to be buzzword- and jargon-fueled romp through current topics in venture capital. Alex will review some of the basics of the venture-capital industry and cover a number of areas Battery is focused on in its practice investing across enterprise IT and developer tools/platforms. He will also provide some advice... Read More

Shasank Chavan (Oracle)

Speaker:

Shasank Chavan

Analytic workloads in data management systems are dominated by joins, aggregations, scan and filtering costs. In-Memory columnar databases have significantly optimized scans using compressed data formats and SIMD vectorization techniques, but have made little impact to the rest of the query execution plan. The Oracle Database In-Memory (DBIM) Option introduced new SQL execution operators that accelerate a wide range of... Read More

[DB Seminar] Fall 2017: Angela Jiang

Speaker:

Angela Jiang

Mainstream adaptively merges the video stream processing of concurrent applications sharing fixed edge resources to maximize aggregate result quality. Mainstream’s approach enables partial-DNN compute sharing among applications using DNNs (deep neural networks) that are fine-tuned from the same base model, decreasing aggregate per-frame compute time. Moreover, since the choice depends on the mix of applications running on an edge node,... Read More

Smooth Storage : A Distributed Storage System for Managing Structured Time-series Data at Two Sigma (Saurabh Goel)

Speaker:

Saurabh Goel

System:

Smooth

Video:

YouTube

Smooth is a distributed storage system for managing structured time series data at Two Sigma. Smooth's design emphasizes scale, both in terms of size and aggregate request bandwidth, reliability and storage efficiency. It is optimized for large parallel streaming read/write accesses over provided time ranges. Smooth has a clear separation between the metadata and data layers, and supports multiple pluggable... Read More